where \(\mathcal {L}_D^S\)  guarantees the basic identification function of the discriminator.
Furthermore, we use the following bilevel optimization [1]}, [2]} to optimize \(\alpha \) :
i) Update discriminator weights to minimize \(\mathcal {L}_{D}\)  for \(t\)  times.
ii) Update retention factor \(\alpha \)  to minimize \(\mathcal {L}_{arch}\) .
iii) Repeat step i) and step ii) until the end of training.
In the optimization process of the discriminator weights, we freeze the retention factors and vice versa.
